import argparse
import time
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn



from libs.models.experimental import attempt_load
from libs.detect_utils.datasets import LoadStreams, LoadImages
from libs.detect_utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
    apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from libs.detect_utils.plots import colors, plot_one_box
from libs.detect_utils.torch_utils import select_device, load_classifier, time_sync

# def run(weights='yolov5s.pt',  # model.pt path(s)
from libs import onnx_processor

onnxEngin = onnx_processor.initOnnxEngin([320, 320], False)

@torch.no_grad()
def run(weights=r'E:\train-data\labelgo\train40\best.onnx',  # model.pt path(s)
        source=r'E:\train-data\append',  # file/dir/URL/glob, 0 for webcam
        imgsz=320,  # inference size (pixels)
        conf_thres=0.7,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=30,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=True,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project='runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        ):
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://', 'https://'))

    # Directories
    save_dir = increment_path(Path(source) / name, exist_ok=exist_ok)  # increment run
    # Initialize
    set_logging()
    device = select_device(device)
    half &= device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    w = weights[0] if isinstance(weights, list) else weights
    classify, pt, onnx = False, w.endswith('.pt'), w.endswith('.onnx')  # inference type
    stride, names = 320, [f'class{i}' for i in range(1000)]  # assign defaults
    if pt:
        model = attempt_load(weights, map_location=device)  # load FP32 model
        stride = int(model.stride.max())  # model stride
        names = model.module.names if hasattr(model, 'module') else model.names  # get class names
        with open(os.path.join(source,"classes.txt"),"w") as f:
            [f.write(name+"\n") for name in names]

        if half:
            model.half()  # to FP16
        if classify:  # second-stage classifier
            modelc = load_classifier(name='resnet50', n=2)  # initialize
            modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
    elif onnx:
        check_requirements(('onnx', 'onnxruntime'))
        import onnxruntime
        session = onnxruntime.InferenceSession(w, None)
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    if pt and device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        if pt:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
        elif onnx:
            img = img.astype('float32')
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if len(img.shape) == 3:
            img = img[None]  # expand for batch dim

        # Inference
        t1 = time_sync()
        if pt:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(img, augment=augment, visualize=visualize)[0]
            # NMS
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
            t2 = time_sync()

            # Second-stage classifier (optional)
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)
        elif onnx:
            # pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
            pred = onnxEngin.autoLabel_predict(img)
            t2 = time_sync()

        # NMS
        #pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        # t2 = time_sync()

        # Second-stage classifier (optional)
        #if classify:
         #   pred = apply_classifier(pred, modelc, img, im0s)
        # Process predictions
        if pt:
            for i, det in enumerate(pred):  # detections per image
                print(det)
                if webcam:  # batch_size >= 1
                    p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # img.jpg
                txt_path = str(Path(source) / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                imc = im0.copy() if save_crop else im0  # for save_crop
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')

                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')

                            plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                            if save_crop:
                                save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

                # Print time (inference + NMS)
                print(f'{s}Done. ({t2 - t1:.3f}s)')

                # Stream results
                if view_img:
                    cv2.imshow(str(p), im0)
                    cv2.waitKey(1)  # 1 millisecond

                # Save results (image with detections)
                if save_img:
                    if dataset.mode == 'image':
                        cv2.imwrite(save_path, im0)
                    else:  # 'video' or 'stream'
                        if vid_path[i] != save_path:  # new video
                            vid_path[i] = save_path
                            if isinstance(vid_writer[i], cv2.VideoWriter):
                                vid_writer[i].release()  # release previous video writer
                            if vid_cap:  # video
                                fps = vid_cap.get(cv2.CAP_PROP_FPS)
                                w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            else:  # stream
                                fps, w, h = 30, im0.shape[1], im0.shape[0]
                                save_path += '.mp4'
                            vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer[i].write(im0)
        elif onnx:
            print("#############1####")
            pred2 = torch.tensor(pred)
            print(pred2)
            pred2 = pred2[:, pred2.new_tensor(range(pred2.shape[1])) != 0]
            # detections per image
            temp4 = torch.tensor(pred2[:, 4])
            print(temp4)
            temp5 = pred2[:, 5]
            print(temp5)
            pred2[:, 4] = temp5
            pred2[:, 5] = temp4
            print(pred2)
            pred2 = [pred2]
            for i, det in enumerate(pred2):
                # print("#################det ###################")
                # print(det)
                if webcam:  # batch_size >= 1
                    p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

                p = Path(p)  # to Path
                save_path = str(save_dir / p.name)  # img.jpg
                txt_path = str(Path(source) / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                imc = im0.copy() if save_crop else im0  # for save_crop
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')

                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')

                            plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
                            if save_crop:
                                save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

                # Print time (inference + NMS)
                print(f'{s}Done. ({t2 - t1:.3f}s)')

                # Stream results
                if view_img:
                    cv2.imshow(str(p), im0)
                    cv2.waitKey(1)  # 1 millisecond

                # Save results (image with detections)
                if save_img:
                    if dataset.mode == 'image':
                        cv2.imwrite(save_path, im0)
                    else:  # 'video' or 'stream'
                        if vid_path[i] != save_path:  # new video
                            vid_path[i] = save_path
                            if isinstance(vid_writer[i], cv2.VideoWriter):
                                vid_writer[i].release()  # release previous video writer
                            if vid_cap:  # video
                                fps = vid_cap.get(cv2.CAP_PROP_FPS)
                                w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            else:  # stream
                                fps, w, h = 30, im0.shape[1], im0.shape[0]
                                save_path += '.mp4'
                            vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer[i].write(im0)

    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')



